Update README.md

README.md

@@ -4,9 +4,9 @@
 
 This is the code repository for OctSurf: Efficient Hierarchical Voxel-based Molecular Surface Representation for the Protein-Ligand Affinity Prediction.
 
-### Experiments
+## Experiments
 
-#### PDBbind Download  
+### PDBbind Download  
 Download PDBbind general, refined, and core(CASF) from http://www.pdbbind.org.cn.   
 And fix some minor problems(replace several mol2 files by transforming sdf in general set, and remove the CONECT with index 0 pdb file). 
 ```angular2
@@ -15,7 +15,7 @@ bash data_download.sh
 cd ..
 ```
 
-#### Set-up enviroment
+### Set-up enviroment
 Install packages and compile the required tools, e.g. the java tool for generating surface points, the C++ code for octree, and the operation (convolution etc.) API for tensorflow.
 ```angular2
 # compile java
@@ -45,7 +45,7 @@ python build.py
 cd ../../
 ```
 
-#### Octree Generation Example
+### Octree Generation Example
 Provide one example data 1A1E, also in refined-set.    
 Following steps can generate the points and build the OctSurf. (Default density for points is 6, and depth for OctSurf is 10.)    
 We also provide python tool to parse the generated OctSurf, and visualize it by generating vtk files that can visualize in Paraview.    
@@ -63,29 +63,29 @@ cd python
 python octree_parse.py
 cd ../
 ```
-#### CNN modeling
-- prepare the data   
+### CNN modeling
+#### prepare the data for modeling   
 	First it will generate the points file for each complex in general, refined, core set. (The density of points can be specificed, low resolution OctSurf can use low density points to accelerate the process, here for depth=6 model, we use density 3. Can be specified in .sh file)   
 	Then the points and labels will be transformed into tfrecords file.   
 ```angular2
 bash data_prepare_model.sh
 cd ..
 ```
-- train model   
+#### train model   
 	Specify the config files (the network architecture, the input/log path, iterations etc.)  
 ```angular2
 cd tensorflow/script
 python run_cls.py --config configs/train_resnet_depth6.yaml
 ```
 
-- test performance    
+#### test performance    
 	Specify the config files (the path for pretrained model/test dataset, network architecture, iterations etc)  
 	Test the pre-trained model on test dataset, and report the performance.  
 ```angular2
 python test_reg_ave.py --config configs/test_resnet_depth6.yaml
 ```
 
-### Acknowledgments
+## Acknowledgments
 Code is inspired by [O-CNN](https://wang-ps.github.io/O-CNN.html).
 
 The code is released under the **MIT license**.